Using genetic algorithms and linear regression analysis for private housing demand forecast

• Published in: Building and environment 2008-06, Vol.43 (6), p.1171-1184
• Main Authors: Thomas Ng, S., Skitmore, Martin, Wong, Keung Fai
• Format: Article
• Language: English
• Subjects: Applied sciences; Buildings; Buildings. Public works; Computation methods. Tables. Charts; Demand; Exact sciences and technology; Forecasting; Genetic algorithm; Housing; Private sector; Residential building; Structural analysis. Stresses; Supply; Types of buildings
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2007.02.017

An accurate prediction of prospective construction supply and demand, especially the private residential market, is paramount important to policy makers, as it could help formulate strategies to cultivate/stabilize the economy and satisfy the social needs (at macro level). Despite that, a realistic prediction of future private residential demand is never an easy task, as it is governed by a number of social and economic factors. In this paper, four leading indicator models are developed and compared for directly forecasting Hong Kong private sector residential demand. These comprise a (i) Linear Regression Analysis (LRA) model, (ii) Genetic Algorithms (GA) model, (iii) GA-LRA model, where LRA is used to select the indicator variables; and (iv) GA-LRA model with Adaptive Mutation Rate (AMR) to reduce the likelihood of local optima. The findings indicate that the GA-LRA model with AMR provides the most accurate forecasts and over a longer time horizon. In providing a range of possible forecasts, the model also provides an opportunity for the decision-maker to exercise judgment in selecting the most appropriate forecasts.